Title |
Stem cell models of Alzheimer’s disease: progress and challenges
|
---|---|
Published in |
Alzheimer's Research & Therapy, June 2017
|
DOI | 10.1186/s13195-017-0268-4 |
Pubmed ID | |
Authors |
Charles Arber, Christopher Lovejoy, Selina Wray |
Abstract |
A major challenge to our understanding of the molecular mechanisms of Alzheimer's disease (AD) has been the lack of physiologically relevant in vitro models which capture the precise patient genome, in the cell type of interest, with physiological expression levels of the gene(s) of interest. Induced pluripotent stem cell (iPSC) technology, together with advances in 2D and 3D neuronal differentiation, offers a unique opportunity to overcome this challenge and generate a limitless supply of human neurons for in vitro studies. iPSC-neuron models have been widely employed to model AD and we discuss in this review the progress that has been made to date using patient-derived neurons to recapitulate key aspects of AD pathology and how these models have contributed to a deeper understanding of AD molecular mechanisms, as well as addressing the key challenges posed by using this technology and what progress is being made to overcome these. Finally, we highlight future directions for the use of iPSC-neurons in AD research and highlight the potential value of this technology to neurodegenerative research in the coming years. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 14 | 52% |
United States | 3 | 11% |
Unknown | 10 | 37% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 14 | 52% |
Scientists | 9 | 33% |
Practitioners (doctors, other healthcare professionals) | 2 | 7% |
Science communicators (journalists, bloggers, editors) | 2 | 7% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 419 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 78 | 19% |
Student > Bachelor | 61 | 15% |
Researcher | 55 | 13% |
Student > Master | 47 | 11% |
Student > Doctoral Student | 18 | 4% |
Other | 43 | 10% |
Unknown | 117 | 28% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 80 | 19% |
Neuroscience | 75 | 18% |
Agricultural and Biological Sciences | 54 | 13% |
Engineering | 18 | 4% |
Medicine and Dentistry | 17 | 4% |
Other | 46 | 11% |
Unknown | 129 | 31% |